VMware vSphere Bitfusion virtualizes hardware accelerators such as graphical processing units (GPUs) to provide a pool of shared, network-accessible resources that support artificial intelligence (AI) and machine learning (ML) workloads. vSphere Bitfusion works with artificial intelligence frameworks such as TensorFow and PyTorch. You can deploy vSphere Bitfusion within a virtual machine or Docker container for use in data center environments. With vSphere Bitfusion, you can monitor health, utilization, efficiency, and availability of all GPU servers in the network. You can also monitor client consumption of GPUs and assign quotas and time limits.
Want to know what is in the current release of vSphere Bitfusion? Look at the latest vSphere Bitfusion release notes.
Learn About Some of Our vSphere Bitfusion Features
To learn about the following topics, read the vSphere Bitfusion Installation Guide.
- Learn the basic concepts of vSphere Bitfusion, and how it shares GPU access with AI and ML applications.
- Understand the system requirements necessary to operate vSphere Bitfusion in your environment.
- Learn how to install the vSphere Bitfusion appliance and Bitfusion client software.
To learn how to operate vSphere Bitfusion and run AI and ML applications, read the vSphere Bitfusion User Guide.
- Learn how to configure the vSphere Bitfusion client and run applications using the command-line interface.
- Understand how you monitor and manage a vSphere Bitfusion deployment.
- Learn how to use the vSphere Bitfusion graphical user interface within the vSphere Client to view current and historical statistics of GPU allocation and usage, network traffic statistics, and logging and health reports.
- Learn how to run ML applications on remote, shared GPUs.
To learn about using TensorFlow with vSphere Bitfusion, read the Running TensorFlow on Bitfusion Example Guide.
- Learn how to install NVIDIA CUDA and NVIDIA CUDA Deep Neural Network library (cuDNN). CUDA is a computing library developed by NVIDIA that enables general computing on GPUs. cuDNN is a GPU-accelerated library of primitives for use with deep neural networks.
- Understand how to install and use TensorFlow and TensorFlow Benchmarks with Bitfusion.
Download vSphere Bitfusion
Download the following software packages to begin your vSphere Bitfusion deployment.
- Download vSphere Bitfusion appliance and vSphere Bitfusion client software.
- Download the NVIDA Driver for Red Hat Linux.
- Download the NVIDA Driver for Ubuntu Linux.
Explore Our Videos
You can learn about deploying and operating vSphere Bitfusion by reading the documentation, and by watching videos on the VMware vSphere YouTube channel.
Learn More About vSphere Bitfusion
- Learn more about vSphere Bitfusion by visiting vSphere Bitfusion Solutions.
- Learn about TensorFlow, an end-to-end open source platform for machine learning. TensorFlow makes it easy to create machine learning models for desktop, mobile, web, and cloud environments.
- Bitfusion integrates with CUDA, a parallel computing platform developed by NVIDIA for general computing on GPUs. With CUDA, you can dramatically speed up computing applications by harnessing the power of GPUs. Applications developed with CUDA have been deployed to GPUs in embedded systems, workstations, data centers, and in the cloud.
- Understand how NVIDIA cuDNN, a GPU-accelerated library of primitives for use with deep neural networks, integrates with Bitfusion to accelerate GPU performance, allowing you to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning.
Use vSphere Bitfusion Documentation
The vSphere Bitfusion 2.0 documentation consists of a set of PDF documents that you can download. These documents focus on how to install, manage, and use the product. You can also find FAQs and other specialized documents in the Resources section of the vSphere Bitfusion Solutions web page.